Feynman: Knowledge-Infused Diagramming Agent for Scalable Visual Designs
arXiv:2603.12597v1 Announce Type: new Abstract: Visual design is an essential application of state-of-the-art multi-modal AI systems. Improving these systems requires high-quality vision-language data at scale. Despite the abundance of internet image and text data, knowledge-rich and well-aligned image-text pairs are rare. In this paper, we present a scalable diagram generation pipeline built with our agent, Feynman. To create diagrams, Feynman first enumerates domain-specific knowledge components (''ideas'') and performs code planning based on the ideas. Given the plan, Feynman translates ideas into simple declarative programs and iterates to receives feedback and visually refine diagrams. Finally, the declarative programs are rendered by the Penrose diagramming system. The optimization-based rendering of Penrose preserves the visual semantics while injecting fresh randomness into the layout, thereby producing diagrams with visual consistency and diversity. As a result, Feynman can a
arXiv:2603.12597v1 Announce Type: new Abstract: Visual design is an essential application of state-of-the-art multi-modal AI systems. Improving these systems requires high-quality vision-language data at scale. Despite the abundance of internet image and text data, knowledge-rich and well-aligned image-text pairs are rare. In this paper, we present a scalable diagram generation pipeline built with our agent, Feynman. To create diagrams, Feynman first enumerates domain-specific knowledge components (''ideas'') and performs code planning based on the ideas. Given the plan, Feynman translates ideas into simple declarative programs and iterates to receives feedback and visually refine diagrams. Finally, the declarative programs are rendered by the Penrose diagramming system. The optimization-based rendering of Penrose preserves the visual semantics while injecting fresh randomness into the layout, thereby producing diagrams with visual consistency and diversity. As a result, Feynman can author diagrams along with grounded captions with very little cost and time. Using Feynman, we synthesized a dataset with more than 100k well-aligned diagram-caption pairs. We also curate a visual-language benchmark, Diagramma, from freshly generated data. Diagramma can be used for evaluating the visual reasoning capabilities of vision-language models. We plan to release the dataset, benchmark, and the full agent pipeline as an open-source project.
Executive Summary
This article presents Feynman, a novel knowledge-infused diagramming agent designed to generate scalable visual designs. Feynman leverages domain-specific knowledge components to create diagrams, iteratively refining them based on feedback. The agent's output is then rendered using the Penrose diagramming system, resulting in visually consistent and diverse diagrams. A dataset of over 100,000 well-aligned diagram-caption pairs was synthesized using Feynman, along with a visual-language benchmark called Diagramma. This breakthrough has significant implications for vision-language models, enabling the evaluation of their visual reasoning capabilities. The authors plan to release the dataset, benchmark, and agent pipeline as an open-source project.
Key Points
- ▸ Feynman is a knowledge-infused diagramming agent designed to generate scalable visual designs
- ▸ Feynman leverages domain-specific knowledge components to create diagrams
- ▸ The agent's output is rendered using the Penrose diagramming system for visually consistent and diverse diagrams
Merits
Strength
Feynman's ability to generate high-quality visual designs at scale addresses a significant challenge in the field of vision-language systems. The agent's capacity to learn from feedback and iteratively refine diagrams enables the creation of visually consistent and diverse outputs.
Demerits
Limitation
The article does not provide a comprehensive evaluation of Feynman's performance compared to existing diagramming agents. Further research is needed to assess the agent's robustness and generalizability across different domains and tasks.
Expert Commentary
The article presents a novel and promising approach to generating scalable visual designs using Feynman. The agent's ability to learn from feedback and iteratively refine diagrams is a significant strength. However, further research is needed to assess Feynman's performance and robustness compared to existing diagramming agents. The creation of a visual-language benchmark, Diagramma, is a significant contribution to the field, enabling the evaluation of vision-language models' visual reasoning capabilities. The implications of Feynman's technology extend beyond academia, with potential applications in education, scientific visualization, and data analysis.
Recommendations
- ✓ Further research is needed to evaluate Feynman's performance and robustness compared to existing diagramming agents.
- ✓ The authors should consider releasing a more comprehensive evaluation of Feynman's performance, including comparisons to existing models and assessments of the agent's generalizability across different domains and tasks.
